Leonard Lobel

Leonard Lobel (Microsoft MVP, Data Platform) is the chief technology officer and co-founder of Sleek Technologies, Inc., a New York-based development shop with an early adopter philosophy toward new technologies. He is also a principal consultant at Tallan, Inc., a Microsoft National Systems Integrator and Gold Competency Partner.

Programming since 1979, Lenni specializes in Microsoft-based solutions, with experience that spans a variety of business domains, including publishing, financial, wholesale/retail, health care, and e-commerce. Lenni has served as chief architect and lead developer for various organizations, ranging from small shops to high-profile clients. He is also a consultant, trainer, and frequent speaker at local usergroup meetings, VSLive, SQL PASS, and other industry conferences.

Lenni has also authored several MS Press books and Pluralsight courses on SQL Server programming

I want to…

To begin, let’s be clear that an Azure Cosmos DB container can have only one partition key. I say this from the start in case “multiple partition keys” in the title is somehow misinterpreted to imply otherwise. You always need to come up with a single property to serve as the partition key for every container and choosing the best property for this can sometimes be difficult.

Understanding the Problem

Making the right choice requires intimate knowledge about the data access patterns of your users and applications. You also need to understand how horizontal partitioning works behind the scenes in terms of storage and throughput, how queries and stored procedures are scoped by partition key, and the performance implications of running cross-partition queries and fan-out queries. So there are many considerations to take into account before settling on the one property to partition a container on. I discuss all this at length in a previous blog post Horizontal Partitioning in Azure Cosmos DB.

But here’s the rub. What if, after all the analysis, you come to realize that you simply cannot settle on a single property that serves as an ideal partition key for all scenarios? Let’s say for example, from a write perspective, you find one property will best distribute writes uniformly across all the partitions in the container. But from a query perspective, you find that using the same partition key results in too much fanning out. Or, you might identify two categories of common queries, where it’s roughly 50/50; meaning, about half of all the queries are of one type, and half are of the other. What do you do if the two query categories would each benefit from different partition keys?

Your brain can get caught in an infinite loop over this until you wind up in that state of “analysis paralysis,” where you recognize that there’s just no single best property to choose as the partition key. To break free, you need to think outside the box. Or, let’s say, think outside the container. Because the solution here is to simply create another container that’s a complete “replica” of the first. This second container holds the exact same set of documents as the first but defines a different partition key.

I placed quotes around the word “replica” because this second container is not technically a replica in the true Cosmos DB sense of the word (where, internally, Cosmos DB automatically maintains replicas of the physical partitions in every container). Rather, it’s a manual replica that you maintain yourself. Thus, it’s your job to keep it in sync with changes when they happen in the first container, which is partitioned by a property that’s optimized for writes. As those writes occur in real time, you need to respond by updating the second collection, which is partitioned by a property that’s optimized for queries.

Enter Change Feed

Fortunately, change feed comes to the rescue here. Cosmos DB maintains a persistent record of changes for every container that can be consumed using the change feed. This gives you a reliable mechanism for retrieving changes made to any container, all the way back to the beginning of time. For an introduction to change feed, have a look at my previous blog post Change Feed – Unsung Hero of Azure Cosmos DB

In this three-part series of blog posts, I’ll dive into three ways you can use the change feed to implement a solution for synchronizing containers:

Querying the change feed directly

Using the Change Feed Processor (CFP) library

Writing an Azure Functions Cosmos DB trigger

Let’s get started. Assume that we’ve done our analysis and established that city is the ideal partition key for writes, as well as roughly half of the most common queries our users will be running. But we’ve also determined that state is the ideal partition key for the other (roughly half) commonly executed queries. This means we’ll want one container partitioned by city, and another partitioned by state. And we’ll want to consume the city-partitioned container’s change feed to keep the state-partitioned container in sync with changes as they occur. We’ll then be able to direct our city-based queries to the first container, and our state-based queries to the second container, which then eliminates fan-out queries in both cases.

Setting Up

If you’d like to follow along, you’ll need to be sure your environment is setup properly. First, of course, you’ll need to have a Cosmos DB account. The good news here is that you can get a free 30-day account with the “try cosmos” offering, which doesn’t even require a credit card or Azure subscription (just a free Microsoft account). Even better, there’s no limit to the number of times you can start a new 30-day trial. Create your free account at http://azure.microsoft.com/try/cosmosdb.

You’ll need your account’s endpoint URI and master key to connect to Cosmos DB from C#. To obtain them, head over to your Cosmos DB account in the Azure portal, open the Keys blade, and keep it open so that you can handily copy/paste them into the project.

You’ll also need Visual Studio. I’ll be using Visual Studio 2019, but the latest version of Visual Studio 2017 is fine as well. You can download the free community edition at https://visualstudio.microsoft.com/downloads.

Querying the Change Feed Directly

We’ll begin with the raw approach, which is to query the change feed directly using the SDK. The reality is that you’ll almost never want to go this route, except for the simplest small-scale scenarios. Still, it’s worth taking some time to examine this approach first, as I think you’ll benefit from learning how the change feed operates at a low level, and it will enhance your appreciation of the Change Feed Processor (CFP) library which I’ll cover in the next blog post of this series.

Fire up Visual Studio, create a new .NET Core console application, and name the project ChangeFeedDirect, and name the solution ChangeFeedDemos (we’ll be adding more projects to this solution in parts 2 and 3 of this blog series). Next, add the SDK to the ChangeFeedDirect project from the NuGet package Microsoft.Azure.DocumentDB.Core:

We’ll write some basic code to create a database with the two containers, with additional methods to create, update, and delete documents in the first container (partitioned by city). Then we’ll write our “sync” method that directly queries the change feed on the first container, in order to update the second container (partitioned by state) and reflect all the changes made.

Note: Our code (and the SDK) refers to containers as collections.

We’ll write all our code inside the Program.cs file. First, update the using statements at the very top of the file to get the right namespaces imported:

using Microsoft.Azure.Documents;
using Microsoft.Azure.Documents.Client;
using Newtonsoft.Json;
using System;
using System.Collections.Generic;
using System.Linq;
using System.Threading.Tasks;

Next, at the very top of the Program class, set two private string constants for your Cosmos DB account’s endpoint and master key. You can simply copy them from the Keys blade in the Azure portal, and paste them right into the code:

Most of this code is intuitive, even if you’ve never done any Cosmos DB programming before. We first use our endpoint and master key to create a DocumentClient, and then we use the client to create a database named multipk with the two containers in it.

This works by first calling DeleteDatabaseAsync wrapped in a try block with an empty catch block. This effectively results in “delete if exists” behavior to ensure that the multipk database does not exist when we call CreateDatabaseAsync to create it.

Next, we call CreateDocumentCollection twice to create the two containers (again, a collection is a container). We name the first container byCity and assign it a partition key of /city, and we name the second container byState and assign /state as the partition key. Both containers reserve 400 request units (RUs) per second, which is the lowest throughput you can provision.

Notice the DefaultTimeToLive = -1 option applied to the first container. At the time of this writing, change feed does not support deletes. That is, if you delete a document from a container, it does not get picked up by the change feed. This may be supported in the future, but for now, the TTL (time to live) feature provides a very simple way to cope with deletions. Rather than physically deleting documents from the first container, we’ll just update them with a TTL of 60 seconds. That gives us 60 seconds to detect the update in the change feed, so that we can physically delete the corresponding document in the second container. Then, 60 seconds later, Cosmos DB will automatically physically delete the document from the first container by virtue of its TTL setting. You’ll see all this work in a moment when we run the code.

The other point to call out is the creation of our sync document, which is a special metadata document that won’t get copied over to the second container. Instead, we’ll use it to persist a timestamp to keep track of the last time we synchronized the containers. This way, each time we sync, we can request the correct point in time from which to consume changes that have occurred since the previous sync. The document is initialized with a lastSync value of null so that our first sync will consume the change feed from the beginning of time. Then lastSync is updated so that the next sync picks up precisely where the first one left off.

Now let’s implement CreateDocuments. This method simply populates three documents in the first container:

Notice that all three documents have city and state properties, where the city property is the partition key for the container that we’re creating these documents in. The state property is the partition key for the second container, where our sync method will create copies of these documents as it picks them up from the change feed. The slogan property is just an ordinary document property. And although we aren’t explicitly supplying an id property, the SDK will automatically generate one for each document with a GUID as the id value.

We’ll also have an UpdateDocument method to perform a change on one of the documents:

Remember that (currently) the change feed doesn’t capture deleted documents, so we’re using the TTL (time to live) technique to keep our deletions in sync. Rather than calling DeleteDocumentAsync to physically delete the Orlando document, we’re simply updating it with a ttl property set to 60 and saving it back to the container with ReplaceDocumentAsync. To the change feed, this is just another update, so our sync method will pick it up normally as you’ll see in a moment. Meanwhile, Cosmos DB will physically delete the Orlando document from the first container in 60 seconds, giving our sync method up to one minute to pick it up from the change feed and delete it from the second container.

And finally, the sync method, which is what this whole discussion is all about. Here’s the code for SyncCollections:

Let’s break this down. First, we grab the last sync time from the sync document in the first container. Remember, this will be null the very first time we run this method. Then, we’re ready to query the change feed, which is a two-step process.

For step 1, we need to discover all the partition key ranges in the container. A partition key range is essentially a set of partition keys. In our small demo, where we have only one document each across three distinct partition keys (cities), Cosmos DB will host all three of these documents inside a single partition key range.

Although there is conceptually only one change feed per container, there is actually one change feed for each partition key range in the container. So step 1 calls ReadPartitionKeyRangeFeedAsync to discover the partition key ranges, with a loop that utilizes a continuation token from the response so that we retrieve all of the partition key ranges into a list.

Then, in step 2, we iterate the list to consume the change feed on each partition key range. Notice the ChangeFeedOptions object that we set on each iteration, which identifies the partition key range in PartitionKeyRangeId, and then sets either StartFromBeginning or StartTime, depending on whether lastSync is null or not. If it’s null (which will be true only on the very first sync), then StartFromBeginning will be set to true and StartTime will be set to null. Otherwise, StartFromBeginning gets set to false, and StartTime gets set to the timestamp from the last sync.

After preparing the options, we call CreateDocumentChangeFeedQuery that returns an iterator. As long as the iterator’s HasMoreResults property is true, we call ExecuteNextAsync on it to fetch the next set of results from the change feed. And here, ultimately, is where we plug in our sync logic.

Each result is a changed document. We know this will always include the sync document, because we’ll be updating it after every sync. This is metadata that we don’t need copied over to the second container each time, so we filter out the sync document by testing the city property for “sync.”

For all other changed documents, it now becomes a matter of performing the appropriate create, update, or delete operation on the second container. First, we check to see if there is a ttl property on the document. Remember that this is our indication of whether this is a delete or not. If the ttl property isn’t present, then it’s either a create or an update. In either case, we handle the change by calling UpsertDocumentAsync on the second container (upsert means “update or insert”).

Otherwise, if we detect the ttl property, then we call DeleteDocumentAsync to delete the document from the second container, knowing that Cosmos DB will delete its counterpart from the first container when the ttl expires.

Let’s test it out. Start the console app and run the DB (create database) and CD (create documents) commands. Then navigate to the Data Explorer in the Azure portal to verify that the database exists with the two containers, and that the byCity container has three documents in it, plus the sync document with a lastSync value of null indicating that no sync has yet occurred:

The byState container should be empty at this point, because we haven’t run our first sync yet:

Back in the console app, run the SC command to sync the containers. This copies all three documents from the first container’s change feed over to the second container, skipping the sync document which we excluded in our code:

Refresh the second container view, and you’ll see that the slogan property has now been updated there as well.

Finally, run the DD command in the console app the delete the Orlando, FL document from the first container. Remember that this doesn’t actually delete the document, but rather updates it with a ttl property set to 60 seconds. Then run SC to sync the containers again:

You can now confirm that the Orlando, FL document is deleted from the second container, and within a minute (upon ttl expiration), you’ll see that it gets deleted from the first container as well.

However, don’t wait longer than a minute after setting the ttl before running the sync or you will run out of time. Cosmos DB will delete the document from the first container when the ttl expires, at which point it will disappear from the change feed and you will lose your chance to delete it from the second container.

What’s Next?

It didn’t take that much effort to consume the change feed, but that’s only because we have a tiny container with just a handful of changes, and we’re manually invoking each sync. To consume the change feed at scale, much more work needs to be done. For example, the change feed on each partition key range of the container can be consumed concurrently, so we could add multithreading logic to parallelize those queries. Long change feeds can also be consumed in chunks, using continuation tokens that we could persist as a “lease,” so that new clients can resume consumption where previous clients left off. We also want the sync automated, so that we don’t need to poll manually.

Fortunately, the Change Feed Processor (CFP) library handles all these details for you. It was certainly beneficial to start by querying the change feed directly, since exploring that option first is a great way to learn how change feed works internally. However, unless you have very custom requirements, the CFP library is the way to go.

So stay tuned for part 2, and we’ll see how much easier it is to implement our multiple partition key solution much more robustly using the CFP library.